This textbook covers logical and relational learning in depth, and hence provides an introduction to inductive logic programming (ILP), multirelational data mining (MRDM) and (statistical) relational learning (SRL). These subfields of data mining and machine learning are concerned with the analysis of complex and structured data sets that arise in numerous applications, such as bio- andchemoinformatics, network analysis, Web mining, and natural language processing, within the rich representations offered by relational databases and computational logic. The author introduces the machine learning and representationalfoundations of the field and explains some important techniques in detail by using some of the classic case studies centered around well-known logical and relational systems. First textbook on multi-relational data mining and inductive logic programming Self-contained and easily accessible Provides a complete overview of the field INDICE: Introduction.- An Introduction to Logic.- An Introduction to Learning and Search.- Representations for Mining and Learning.- Generality and Logical Entailment.- The Upgrading Story.- Inducing Theories.- Probabilistic LogicLearning.- Kernels and Distances for Structured Data.- Computational Aspects of Logical and Relational Learning.- Conclusions.- References.- Author Index.-Subject Index.
- ISBN: 978-3-540-20040-6
- Editorial: Springer
- Encuadernacion: Cartoné
- Páginas: 385
- Fecha Publicación: 01/08/2008
- Nº Volúmenes: 1
- Idioma: Inglés